#005: Big Data -- What an Executive Needs to Know

The power of big data is a curious thing, Make a one man weep, make another man sing. Change a hawk to a little white dove. More than a feeling that’s the power of big data. As always, Huey Lewis hits the nail on the head with this complex topic. What does the phrase actually mean? How can my company take advantage of it? Michael, Tim and Jim take on big data in episode five, and try to focus in on making this hard to pin down concept understandable and relevant. All this and more in one American hour, 46 Canadian minutes.


Episode Transcript

The following is a straight-up machine translation. It has not been human-reviewed or human-corrected. We apologize on behalf of the machines for any text that winds up being incorrect, nonsensical, or offensive. We have asked the machine to do better, but it simply responds with, “I’m sorry, Dave. I’m afraid I can’t do that.”

[00:00:25] Hello. Welcome to the digital analytics power hour. This is Episode 5.

[00:00:31] You know you’re an executive or maybe you worked for an executive. But one thing your executive or you have heard about is big data. That’s today’s topic. Let’s dig in to big data. It usually comes with a big price and a big promise. And we’re here today to talk about some of that. Obviously I’m going to be joined today by my two other hosts Tim Wilson and partner at web analytics demystified. Hey Michael. And Jim Kane world traveler and double CEO of Babbage systems and napkin Inc. Hello gents. And obviously I’m Michael Helling the analytics practice leader at search discovery in Atlanta Georgia. All right guys. Never before have such great guys undertaken a task for which they were so not prepared. No no. In the realm of big data we definitely have a lot of a lot of noise out there and you know when you really start to test it sometimes it seems like the promise is too good to be true. And the excitement around it is a little more hype than I can’t think of an H word but basically substance right. So guys maybe to start the show let’s dig into the topic or the definition of big data and start there. But so let me hand it off to you guys. Who wants to take it first. Yeah.

[00:02:04] I’m perfect because I can.

[00:02:07] I mean so to me big data. It is a term that.

[00:02:11] Anybody and everybody has free license to use however they want. And so I kind of get struck when I hear a a. And I think there’s even a kind of a standard.

[00:02:21] Kind of normal way for people to type up a data saying Big Data is not just more data but a lot of times that’s what it gets treated as is Big Data is more data. So I think there are probably two or three or are there’s a big data number of competing definitions for what their data is but one person who kind of talked about it and struck me as being kind of unique.

[00:02:43] But I think he was kind of being contrarian was you know Gary Angell from symphonic or Ernst and Young and he said I’m a completely butcher his definition. He kind of goes through the four V’s a bit of data and says look it’s not because it’s just more data. It’s not because it’s real time. He said that to him the big distinguishing characteristic is that it’s sort of the it’s the combination of the sequence of things happening and the volume of what we actually know about what’s happening which made me which made me think a little bit but yet when I go to Wikipedia or when we go to Webster’s or wherever. I don’t know what that definition is necessarily there. Yeah I think maybe now it’s your turn to define big data.

[00:03:33] It’s just interesting that three guys that love to have an opinion all dodged on being the first to say what it means you know because well everyone knows that whoever opens her mouth first it’s going to be the primary target of all the backlash or the colony vendor for all the trashing. But I mean the way that I describe big data to people is just all the eggs one basket you know. I mean I know it’s kind of a sales guy Ed. But you take all the data in your business and all the data that you’d like to capture and you put it all in one spot then you can reap the benefit of the network effect of all of those things in one place and I don’t think it’s really that much more complicated than that until you start getting to very specific use cases and verticals and types of data. You know all your eggs in one basket.

[00:04:17] My nuts look well it seems like there’s a lot of the definition is around the technology that you’ve got when Hadoop comes out when there’s some level of definition that that big data is when you’ve got more data. So if you put it all in one basket but then you don’t have the horsepower to actually crunch it because you can’t just throw it into a big you know flat tables. So you put it into Hadoop into some new sort of data warehouse that they confronted and it kind of sticks in my craw when it gets defined as something that requires more technology or a different approach to. Crunch it. So I do feel like there’s a legitimate definition. It’s that it’s more than that and that’s where the definitions are.

[00:05:02] We did Google it and Google says it’s extremely large data sets that may be analyzed computationally to reveal patterns trends and associations especially relating to human behavior and interactions.

[00:05:17] Now what’s interesting is that after you start digging into some of it some of the where the use cases are where the definitions of it. So I hear human behavior and interactions and you think oh this is all about marketing and you read elsewhere in a lot of the talk about big data is no it’s like crazy hyper volumes used for science and engineering and in pattern matching of things that aren’t related to human behavior. Just look at the human behavior part and human humans are so so messy and so noisy and so not repeat are so much easier to predict. You know paramecium is going to do that it is to what what are humans going to do.

[00:05:58] Yeah. And big data you know could be the migratory patterns of birds you know correlated with the ice cream consumption and Motala. You know I mean there is none right now.

[00:06:11] OK.

[00:06:11] Well yeah. But yeah I mean so is it going to be a pretty lame show if we can’t move past the definition though.

[00:06:20] Can we agree with my statement that all the data needs to be in one spot. What are the due to go to seven different disparate data sources and do we know it’s all going to be one spot for it to be big data right.

[00:06:35] I don’t know. I think so.

[00:06:37] I feel like that this description of data warehousing which could definitely be part of big data. But that’s the thing is I think maybe Big Data tries to not end with what is your data store look like it tries to keep going into and we’re going to apply these sorts of computational analysis to it and we’re going to apply these kinds of algorithms to it and big data becomes something well beyond sort of like the data itself. It’s the data itself and the functions behind it. And maybe that’s really where the problem lies as people are describing multiple fields in terms of where data gets stored and how it’s stored and how much of it there is and the methods used to kind of parse and function within that data you know to to to analyze it.

[00:07:26] I wonder if some of it is the evolution that if you look at how we’ve got to to this concept of big data was this explosion and it wasn’t it wasn’t marketing and digital and the interaction can capture.

[00:07:39] But I think it’s also in instrumentation and the ability to you know record temperature changes at the nanosecond for all I know and the volumes of data that that that generated. And there there was kind of a march to the date as to not granular enough technology came along to capture more granular data that was seen as a technology problem than it was well if we can capture it we’ve got to start somewhere.

[00:08:05] So that became a storage problem and then it became a. That data is arriving so fast it’s not just storing it it’s actually being able to write it fast enough. And I think that was kind of one generation that that got solved that it was we can now write this stuff fast enough we can capture it and there was an assumption that that was a that was a good thing and then all of a sudden people said oh I want to write a sequel query against this and holy crap I have two million rows in this table that I’m trying to join into two million rows and this other table and I can click go in my queries is going to run for a month and a half and it might not return the right stuff. And that’s when all of a sudden this merger and this is me speculating and a history of the etymology of big data was all of a sudden there was this aha. Oh crap. The challenge was the biggest hurdle wasn’t actually capturing or storing the data. It’s now how do we actually manipulate that data in a way that is reasonably timely. You know I can do a look up on 10000 rows of data in Excel and it can return effectively instantaneously. But when excel when I have those Excel 2007 I think that went up to a million rows and it still worked pretty well.

[00:09:25] But not if I was trying to do the work ups from the million Rosenman million rows and all of a sudden that was kind of so it sort of came from this technological basis that I know in theory I can join this stuff. And so I do tend to agree with Jen that I think there is an implicit assumption the that data has to be kind of aggregated somewhere that is accessible because otherwise there’s no way you’re going to be able to join it. And maybe that’s kind of a key to the definition is that it’s not one data set. It’s joining multiple large datasets and that’s where people perceive the value coming from.

[00:10:03] So is the fact that we’ve been talking for 10 minutes and none of us can pin anything down other than yet probably all in one spot is Big Data a synergy word is it a bridge that gap is it a virtual evangelist word that mean anything.

[00:10:18] Well and I think that’s kind of what we want to discuss right is how do we kind of clear away some of the brush around the actual truth of it. Right. Which is. Well OK so now that you sort of have a working definition maybe of it’s two way radio with making wine. Give me a second. It’s like you know David too big to be analyzed than in classical sequel ways and needs to be held in one key in one kind of data store.

[00:10:48] You know that could be our definition for today of big data. I’m sure there is about five people I’m thinking of right now who are going to believe that’s a woefully inaccurate description. However if we work with that just by itself then we can go back to you and say OK now how do we bring any value to an organization with this. What does it mean to use big data.

[00:11:09] So I think that that is actually with that definition even if it’s wildly imperfect. What that sets up is that yes there are situations where that scenario exists and there is a clear sort of path to value to be gained from it. But that scenario does not apply to ninety five percent of the scenarios where people think it should apply. Yeah when we’re looking at web analytics data is not big data. If we we’re a massive web site and we’re getting the raw files yes. Ymay has big data. Amazon has big data a million dollar a year e-commerce company. I don’t think they do.

[00:11:55] That’s interesting that you say that Tim because you initially brought a Guri angel in his definition of big data I would say actually does apply to just analytics data because he’s talking about it from the concept of the stream of interactions as opposed to a discrete set of aggregated transactions which is more of a classical B.I but I think about it.

[00:12:17] But I think you know you have to have it you have to have sufficient volume that you can actually identify patterns in it. And we’re talking about people and their behavior and it goes back to 10 years ago when people thought that the Clickstream analysis were going to find the most common pass through our site. And you’re you at the most common app. No 1 percent of the time. So that’s good. It’s a fair criticism.

[00:12:39] The recession that we had and I know is one or two episodes ago when Michael was like What do you think about the concept of capture everything now and figured out later and you and I were like hell no hell damn no.

[00:12:51] Like that’s like that’s sloppy analysis. I mean that that concept is pretty much what we’ve already been talking about like that’s where we’ve gotten to in terms of our big data definition. So now I’m wondering if we say you know big data is the ability to provide business value or to answer a business question. Well I think through the application of advanced data mining technology on a significant volume of data that’s in one place like where we’re getting everything that data equals business.

[00:13:22] But hold on a second. I think Big Data is just big data. The business value comes from how you leverage big data because there’s plenty of people who are going to do big data and already are but aren’t getting jack squat in terms of value out of it. It’s not because they’re not trying to do big bailout they’re not using big data or big data stores or whatever you want to call it but that’s that.

[00:13:44] I mean this is kind of where it meets kind of what I think of as okay if it were talking to a sea level executive right now we’ve got 30 seconds to help him understand what he needs to know about big data and how he’s going to apply it to his business. How would we answer that question.

[00:14:00] I think that yeah that simple is you don’t spend a dime until you have articulated a list of problems that it’s going to solve. And I think this was that Michael you’d found this beforehand. The red herring of Big Data the post and I’m trying to scan it now where you know he was saying this is this perception that oh you don’t have to come up with hypotheses you don’t have to ask questions. You just take this massive volume of data and it’s going to find stuff for you. And that that has bugged me. I’m at that point back easily 12 or 13 years to having a debate at a consortium little small thing and hexane am with some other people who said no no no if you just point these things at your big your large data set and they find correlations then you don’t have to come up with hypotheses and that’s a total load of crap that if if I was talking to executive I’d say what what questioning can you not answer. And now let me take that question and say Would Big Data reasonably answer that. And how much am I going to have to invest to get that data. Enjoy that data.

[00:15:13] And I think when you take that target pregnant teen example they had a very specific thing they were trying to do where they said Weeble we have looked at we have enough evidence that these buying patterns are indicative of characteristics of our consumers when they went out and invested in what they were doing and Nordstrom’s kind of the same way they started with their strategy of saying we’re going to empower for time immemorial Nordstrom’s has empowered their employees to have phenomenal customer service. And so they came at it and said How can more data. Further that specific objective and I would guess that they actually sat down and had that document and it clearly defined so they had some direction they didn’t just say hey go out and capture all the data and then we’ll figure out what what to do with it. And this gets to the crux of where the problem is with big data I think which is that this you know magical compu computer savior is going to come along and fix all of our business woes.

[00:16:18] And it’s offense to science a little bit kind of religious fervor that goes into this kind of thing you know. Well I for one welcome our new robot overlords.

[00:16:30] And you know let’s get to your second point the digital technology we’re using to record this podcast and hello to the NSA while we’re at it.

[00:16:40] Yeah. Now I want to Tijuana as not to be so cool you know as you were talking through that you know your example Michael and the thing about Nordstrom’s. Now I wonder I just keep trying to find like because you keep we keep saying I don’t know what the shipping date is. And then if I was talking to an executive I’d say here’s when you can use big data. But we still can’t define it is big data really just stuff a data scientist does. Well then we’d have to define data scientists say when I can do. That’s a whole other podcast. It

[00:17:11] is but I can define it as but I can’t do big data as a data scientist a growth hacker because we could just keep heading down this path. You give it away. Episode 6 which will be all the stupid titles we heard. I

[00:17:28] agree that you can define Big Data and Michael your definition of that is is probably as workable and not controversial as zero you’ll get of one around large datasets being joined together. I think it was a fair take that it doesn’t. Unfortunately there is not part of the definition that it has to deliver business value but that’s what we would want to say to executives that you’ve got to figure out what their business value is or has a high likelihood of being will you act with big data.

[00:18:02] Consider for a moment that that executive is already inundated with reasons why if he’s not on the big data train he’s been left and right his business will suffer because he’s not competing with big data or whatever the terminology of the day is. So then if you’re talking to that guy he’s looking for. Okay everybody is telling me that if I’m not doing big data whenever that means the definition is pretty amorphous as we’ve proven tonight I’m getting left behind. I’m not getting a competitive advantage anymore I’m being left behind by my competitors.

[00:18:38] Yeah but that’s old that’s old school fear uncertainty and doubt selling you know that that’s how large software companies. And I’m not going to name any names. IBM sell significant contracts please. I didn’t hear the names.

[00:18:53] But if the executive comes to me and they say Look tell me what I need and I say if my response is you need a one day off site and we need that one day off site. Exactly two weeks from today and you need to tell everyone who’s going to be an off site that they need to come with a list of large datasets they have and ideas for what those datasets could do. That to me would be reasonable. Hey if you’re scared then what I need is a day of your focused attention. I did a sit down with the white board and we need to draw out what can we actually have are you in financial services. What do we have in the way of credit score data and web behavior data and our own customer record data and geo data that we can that we can stitch together and let’s kind of draw those big boxes that say I’m going to join massive dirty complex data set a massive complex data dataset be it’s a noisy joining it’s probabilistic it’s not it’s not a perfect key. If do that here is something I can do to differentiate. So how much would you charge for a day like that. Tim. The inner voice wasn’t saying Jesus Christ that sound like a console. Oh I don’t care who does that. I mean honestly like I think there probably is when it’s for that. I would not pay for that. I would I would I would pay.

[00:20:26] I would pay for figures to go and have that conversation with an executive who’s not a client because I would actually get to have that moment of saying no you actually need to sit down and be really clear on what your strategy is and what your competitive advantage is or could be. And I will happily do that. I would love to do that.

[00:20:44] Well and I think you know if you meet the three of us being fairly pragmatic fellows typically I think this is what befuddles us the most if I can speak for all of us about sort of where we see big data the enterprise and how it’s being handled. Because the the Freethought around how do we actually get something valuable out of what is actually a pretty big investment seems to be either Merrilee lipservice or almost no planning or thought at all. Maybe that’s the the issue we’re really trying to grapple with and if there is an executive listening that’s our encouragement to you as you know do that upfront thinking I think we’re skirting around the statement that big data in the absence of a really high opportunity cost business case is kind of bullshit.

[00:21:34] Big data is not a thing that you buy for later for the same reason that collecting all your data now because maybe you’ll need it later is a bad idea. And I can give several examples of the last couple of years of being on the phone with a senior decision maker who had just made or was about to make a very expensive consulting or software decision because they felt they need to keep up with the Joneses. And after having a conversation about the time the complexity of the cost and the lack of defined business value we ended up in a conversation you guys have had this conversation before. Member though have you ever seen the Louis C.K. routine where his daughter keeps saying why so is she so she says like why is this guy.

[00:22:16] Why. Why. Why. I don’t remember why. Because I spoke to a lot of Patna High School wags is really unhappy.

[00:22:24] You keep saying why do a senior executive of big data.

[00:22:26] So you know we’re going to spend a ton of money on X Y Zed company well. Well why.

[00:22:33] Well because we need to get into big data worldwide because we’ve got data all over the business. And you get to a point where they go. I’m not sure. Personally I have a specific thing I would like to learn. And three to three times I’ve had that conversation there was a much easier way to solve that problem and there wasn’t a use case at that time for a million dollars to build a you know super hypothesises machine.

[00:23:00] So you’re essentially you’re fired with I’m going to jump into it because I’ve got to start and we had this discussion when we were talking about collect everything.

[00:23:07] It was I’ve gotta start collecting it now because whenever I figure out how to actually crunch it I’m going to need some large amount of history to work with that. And I think that is actually a red herring as well because in the world we live in. The fact is I have never found use and web analytics data that’s three years old. If I am if I’m trying to crunch Big Data of weather patterns absolutely I want to capture more detail and as many data points as possible so that I have models I can feed into because although weather patterns may be shifting historical I can see capturing that data. But for our purposes once you get past almost 13 months right it’s once you’ve gone who looks back at your Carp’s over two holiday seasons ago. It doesn’t happen. So the I’ve got to just focus on capturing the data because I’m going to need as much of it as I possibly can get. I know you. You’re not going to need to wait that long once you started really gathering that larger data set before you’ve got enough for you to kind of mine and work with.

[00:24:14] So I think there’s a lot of different things that could be done with big data and I want to turn our conversation to those. What are the valuable things that businesses can use big data for.

[00:24:25] Well it’s always assumed that we need to kind of scope it to the well would this is the digital analytics power hour.

[00:24:33] So let’s let’s try to stay close to digital data.

[00:24:38] Well I mean I can give you an example. In one scenario we had a customer in apparel and the consumer profile for people who buy in-store because they had a large network of stores. A lot of stores and a catalog and a website. And they did. They spent a lot of money on it. Like online profiling for demographics people who buy in-store totally different demographic profile than the people who buy in the catalog and the people who buy online are totally different as well. So three totally different groups of women interacting with their business. They’re sitting back saying how do we streamline purchasing and marketing so we don’t need to run three separate businesses to me that’s an interesting. That that is a big it’s a it’s a very good question with some key defined outputs. And it’s the kind of scenario where you sit back and you probably hit a wall where you say we need to start thinking about advanced data capture data analysis tools big data so now announcement of what I call what I call Kevin Hillstrom and just get him to give the give the answer to that question in the absence of.

[00:25:46] Big Data.

[00:25:48] I’m not sure that’s a I mean it does as you were talking I’m thinking that is the sort of stuff that Kevin talks about that people talk about omni channel like it’s one consumer freely floating across all these channels and in a lot of ways it tends to not be the case. But if you sit back and say What could we do. Do we understand who these are 20 big data to go out and survey a thousand of each of those and actually understand their attitudes and behaviors and then do some analysis on it and say these are fundamentally different and I can’t or I can’t merge them.

[00:26:25] To me that wouldn’t necessarily qualify as a as I know the surveying is pretty straightforward but then the surveying fed the hypothesis the hypothesis was that if we streamline all of the products we carry in the way we talk about them we should be able to create a more streamlined experience that makes the most money possible with three totally different groups with it. Yeah that’s the big data.

[00:26:55] I think at the end of the day like big data. Well so I’m going to start with a massive oversimplification which is web analytics from digital analytics is mostly about trying to find the difference between two things right. And then Big Data is that same pursuit but also trying to predict the future based on the understanding of that difference. Right. Whether that be the type of customer the type of transactions the type of interactions the behaviors attributes whatever it is about the the things that we’re analyzing.

[00:27:34] There is the potential application of Big Data for predictive analytics can I spit out what levers I can pull and predict how that is going to change business results. I actually think there’s an operational component that this harkens back to some stuff that we tried to do 10 years ago very very ham handedly without the rapid processing but if I’ve got volume attracted at my side and I have IP address and they’re not logged in but I can use 25 different variables to try to kind of hone in on who do I think this person really is and then if I’ve got in-store data and that in-store data is collecting some level of transactional behavioral app usage you know I don’t know if I can take these two data sets and then I can take third party experience data and kind of stitch that onto the households and if I can combine all of that in say I think I can get a critical mass of being able to understand with reasonable certainty who can make offers to take the target the target.

[00:28:46] The quick for anyone listening who hasn’t heard the target example so target the that example of where they were printing their flyers and sending them and they were personalizing the flyers they weren’t personalizing them they were sending different subsets of their fliers to different stores. They had figured out that based on buying patterns they could. They thought they could detect when someone was pregnant.

[00:29:08] A guy goes in and complains to his local Target and says Why are you sending these Firestar House addressed to my daughter that our pregnancy stuff my daughter’s not pregnant. The store manager apologized felt terrible.

[00:29:23] The store manager I guess like followed up a couple of weeks later and said Just following up again wanted to apologize for what happened and the father said. Apparently there were things going on in our house that he didn’t know about. And it turns out my daughter is pregnant. So the upshot was that targets data mining figured out that this guy’s daughter was pregnant before he knew it himself.

[00:29:44] And that is a reasonable use case that we have. We have a finite set of activities that our consumers are doing with us and we think that we can predict future behavior and if we can predict the future behavior or future characteristics of that person then we can make offers to them that they are more likely to respond to.

[00:30:06] But that’s operational. You have to build the model and say yeah this actually holds up it holds up enough but then ultimately the challenge is crunching that data fast enough that you get that customize fliers printed or the customized e-mails made it out the door and there’s still going to be perfect. And those are going to work better than just doing things with kind of spray and pray marketing.

[00:30:28] I agree. But I don’t see how that breaks down my oversimplification at all because we just try not. I don’t think figure out the difference between pregnant and not pregnant and then do something different or understand something different about the consumer at that point. Right.

[00:30:45] Well and I think the thing that Tim did was he also supported your statement about effective Big Data is something that’s future looking like health analysis as Ghost of Christmas present and Ghost of Christmas Past and big data as Ghost of Christmas.

[00:31:01] But I mean is in a certain sense all of that we do in digital analytics is about predicting the future. You know we we look to the past to figure out a path to the future. I mean this is why my studies in history have served me so well. I’m a data historian. Right. I look at that and define and look at the patterns of the past to understand what will happen in the future.

[00:31:25] I would like your linked entitled to change immediately. But I stopped short of that.

[00:31:34] I mean we like to say predictive. I’ll be flat out say I say I don’t necessarily build predictive models but if I can look in the past and say this absolutely did not work. So let’s not do that again.

[00:31:50] That’s kind of different firms. That’s a prediction. Well but I’m not quantifying you’re not quantifiable or not. So maybe that’s the difference being like hey don’t be a dummy and do the same thing as broken versus this will go up by 30 percent if you do X Y and Z. I’m willing to go there with you. That’s good.

[00:32:13] And I think a well executed Big Data practice can accurately predict predict in a way that’s not just directional specific things that are going to happen that will make you more money.

[00:32:23] But I actually disagreeing with your statement like it’s not only future but I think you see this is where we feel like you know the Minority Report thing starts to happen where we’re like yeah the future is all of us sort of like envisioning little things and we’re slicing and dicing data with our fingertips in the sky and you know we’re right back into the computational so savior mode right. Or on our way there.

[00:32:51] So I don’t know that you linked in profile computational savior. Yes. Here’s the thing I heard that I think I want to reiterate and then I want to riff on for just a second and that is figuring out and defining the value of big data. Before you jump in with both feet I’m pretty sure I heard us say that I cannot disagree with that. I think that is Tim will be well sent to your organization for a day and help you do that. Value add for free. You heard it here. No I don’t.

[00:33:27] If you’re at 100 million dollar more organization I will I will pay fifteen hundred dollars to sit down with your CEO and CMO.

[00:33:39] You’re crazy every day but if anyone is listening and has the ability to put those two people in a room and need don’t do that with Tim Wilson You’re a moron.

[00:33:50] I will be sure not parents stress that I will also spend a ton of time prepping for that but I will absolutely love the opportunity.

[00:33:58] But how can you leave. That’s right. But that’s not all. There’s more.

[00:34:03] I just I like doing the kind of periodic recaps. But we started off with no one wanting to. Like with your tent under the bus. I don’t know Tim what do you think big that is. And then I think we’ve gotten to a place where we agree that it’s a massive dataset that lives in probably one place. We’ve also agreed that to justify big data big data requires a business case. Is there anything else that we all agreed should fit into this executive.

[00:34:29] I’m still a fan of the Gyuri Angel School of big data also being defined by not the classic B.I model of how data is analyzed and worked with. So how can that. So yeah that didn’t make it into kind of our nugget of definition from before but it’s a nuance I think deserves people’s attention and there are some really great articles that Gyuri Angel has on his blog that I would highly recommend anyone just to actually think.

[00:35:00] I think it’s a fair point though if you start with your business case and you actually then you are critically evaluated and Jim your example earlier where somebody says we need big data and you start asking why why why go going all Louis C.K. on them.

[00:35:14] They wind up what they articulate doesn’t require anything all that unique it doesn’t require necessarily a massive data set.

[00:35:21] But I think when you get to a y y y that has responded with Oh we do need this stuff. That is a sequence of events and what’s happening in time and you have to take the amount of time in between those events and the combination in the order then if you’ve made the business case that yes there is something at the end of this that would would allow us to make a better decision than your you’re in good shape.

[00:35:51] Let’s go back in and bring something back out again and wrap it up. Jim wrap this up for the executive.

[00:36:00] Well I don’t I don’t know how I’d wrap it up for an executive. You know other than I know the original value proposition and I failed.

[00:36:07] But you know that the conversation or the discussions that we had today were really interesting to me because you know sometimes we agree sometimes we don’t. But we definitely came back to a big data is not a concept that needs to be vaguely embraced by every company that can afford it. You know it’s something that requires us specific use case. It’s something that requires a specific opportunity. You don’t just go OK we’re a hundred million dollar company we should buy some big data. There are specific requirements so I think the discussion with the executive is do you know what big data is. I don’t either. So let’s figure out what you would want to do. I actually I was interviewed by Information Week magazine for a piece I wrote for them like a year and change ago. I asked the guy I said you know you spend all week doing nothing but talking to people in the analytics space. Can you tell me what big data is.

[00:37:01] And he laughs and he’s like you and it’s his job and he’s like that big data you know editor or whatever. So in an executive conversation to me is let me help remove the fear uncertainty and doubt from big data. The other thing is as I think that this is going to dovetail very nicely into our conversation about data science or data scientists because in talking about big data I think I finally understand the value of a data scientist because I’ve historically made fun of them. You guys know that and for people listening in I I have been known to call data scientists the Y2K programmers of 2015 for shame for shame. You know we were talking about the concept of big data and then I think about what a data scientist does. I said it earlier. I still can’t wrap my head around a concise definition of big data. But I can now death definitively tell you the value proposition of a data scientist. I don’t know if that helps.

[00:38:00] So I think what we’ve made some I think some good points. A couple of times but we haven’t explicitly said is big data is not all hype. It’s not a oh big data. You know forget about it. It’s big data is something that in some situations and going forward probably more of those situations. There is business value to be realized but the number of those situations is not as high as a lot of the hype in the press and the executives feel that it is right now. So you might have a situation where big data is going to be worth the investment. But let’s sit down and figure out if that’s really the case. Have that really clear picture in your head. Rather than saying I just got to start doing it and I’ll figure out what I’m going to do with it later.

[00:38:58] Yeah no I think that’s great and that’s kind of what I’m taking away too in a certain sense which is don’t go after big data go after value. And as you go after value you will start to see Big Data happen.

[00:39:11] You may realize you’re you’re avoiding big data you’re avoiding big daddy or avoiding big data. Your taste in business value and all of a sudden you realize you’ve been doing what other people were calling big data. Yeah and you’re you’re a year late into realizing that. Holy crap that’s what we’re doing.

[00:39:25] But the reality is I think that’s the right perspective and that’s an ideal scenario. And obviously there’s a lot more that be said on this topic. And so since there’s so much more to be said we’d love to hear from you on our Facebook page and on Twitter. If you’re an executive with sea level access to CEOs and CMOs and you want to spend the day with Tim Wilson you have an invitation to do that and that’s something to look past.

[00:39:54] So thank you guys for listening everyone and we hope to hear from you on Facebook and on our Twitter accounts so long. And good luck with that. Big data out there.

[00:40:09] Thanks for listening. And don’t forget to join the conversation on Facebook or Twitter. We welcome your comments and questions Facebook dot com slash now or at least now on Twitter.

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